isglobal-brge/EASIER
EwAS: quality control, meta-analysIs and EnRichment

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In isglobal-brge/EASIER: EwAS: quality control, meta-analysIs and EnRichment 

knitr::opts_chunk$set(message = FALSE, warning = FALSE,
                      cache=TRUE)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(knitr)
library(tools)

colorize <- function(x, color) {
  if (knitr::is_latex_output()) {
    sprintf("\\textcolor{%s}{%s}", color, x)
  } else if (knitr::is_html_output()) {
    sprintf("<span style='color: %s;'>%s</span>", color, 
      x)
  } else x
}

Prerequisites

The EASIIER package requires other packages to be installed. These include: ggplot2, VennDiagram, RColorBrewer, tibble, dplyr, stringr, rasterpdf, tidyverse, reshape, ggsignif, tools and meta all available in CRAN. The package also requires other packages from Bioconductor to perform annotations and enrichment : IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, missMethyl, org.Hs.eg.db, GenomicRanges and rtracklayer.

To perform meta-analyses we use GWAMA, a software for meta analysis developed by the Intitute of Genomics from University of Tartu, GWAMA is available at https://genomics.ut.ee/en/tools/gwama-download and must be installed on the computer where we are going to run EASIER. The EASIER R package has been developed run under version R >= 3.6.3 .

Overview

The EASIER package performs downstream analyses of epigenenome-wide association studies (EWAS):

In this vignette we will show how to apply EASIER R package with the EWAS results from three cohorts and two distinct models for each cohort.

Getting started

To install the required packages run the following commands in R:

# Install devtools
install.packages("devtools")

# Install required packages for brgeEnrich package
devtools::source_url("https://raw.githubusercontent.com/isglobal-brge/brgeEnrich/HEAD/installer.R")

# Install brgeEnrich R package
devtools::install_github("isglobal-brge/brgeEnrich@HEAD")

# Install required packages for EASIER package
devtools::source_url("https://raw.githubusercontent.com/isglobal-brge/EASIER/HEAD/installer.R")

# Install EASIER R package
devtools::install_github("isglobal-brge/EASIER@HEAD")

#..# devtools::install_github("isglobal-brge/EASIER@HEAD",  build_opts = c("--no-resave-data", "--no-manual"), build_vignettes = TRUE)

The first command installs a library called devtools that is needed to source online scripts and run them. The next line sources a file that installs all the required packages from R-CRAN and Bioconductor to run EASIER R package. Lastly, the EASIER R package and the corresponding vignettes are installed.

After package is installed, we can load it with command: 

library(EASIER)

library(readtext)

To access to vignette documents you only have to use vignette("EASIER")

Quality control

Quality Control Flowchart

```rQuality control flowchart. This flowchart is used to coduct the quality control of EWAS results (QualityControl.R) ", fig.pos='ht'} include_graphics("imgs/workflows/QCWorkflow.png") 

We have created a script named **[QualityControl.R](https://github.com/isglobal-brge/EASIER/blob/main/test/QualityControl.R)** to carry out the **quality control** of EWAS results automatically, as indicated in the workflow in **Figure\@ref(fig:qcworkflow)**. This script needs some initial editing to indicate information specific to the study. Briefly, after reading the EWAS results file of each cohort and model (study), it starts by detecting duplicated CpGs and CpGs with missing data (NAs), and generates the initial descriptive for Beta, SE and P-value. Then CpGs that meet exclusion criteria (indicated by the user) are excluded from the file, and adjusted p-values and lambda are calculated. A new file with QCed EWAS results for each cohort and model (each study) is created, together with the QCed descriptive for Beta, SE, p-value, and adjusted P-values. Moreover, several plots by cohort and model are generated (distribution plots for SE and P-value, QQ plot, and Volcano plot). Finally, plots comparing QCed EWAS results from different cohorts and models are created (Beta's boxplot and precision plot).



## Initial definition of variables specific to the study


We need to define certain parameters specific to each study in order to run Module 1 - **Quality Control of EWAS results**. `r colorize("(this is the only part of the code that requires editing!).", 'red')`

### Input data

We need to indicate where the files with the EWAS results of each cohort and model (each study) are stored by defining a character vector called 'files'. The example below involves three different cohorts and two distinct models for each cohort:

```r

files <- c('data/PROJ1_Cohort3_Model1_date_v2.txt',
           'data/PROJ1_Cohort3_Model2_date_v2.txt',
           'data/PROJ1_Cohort2_Plate_ModelA1_20170309.txt',
           'data/PROJ1_Cohort2_Plate_ModelA2_20170309.txt',
           'data/Cohort1_Model1_20170713.txt',
           'data/Cohort1_Model2_20170713.txt')

files must contain at least the following variables :

probeID | BETA | SE | P_VAL ------- | ----- | ----- | ----- cg13869341 | 0.00143514362777834 | 0.00963411132344512 | 0.678945643213567 cg24669183 | -0.0215342789035512 | 0.0150948404044624 | 0.013452341234512 cg15560884 | 0.00156725345562218 | 0.0063878467810596 | 0.845523221223523

Where to store output

We need to define the folder where we want to save the QCed EWAS results of each cohort and model, indicating it in a character vector called result_folder. In the example, the QCed files will be stored in a folder named QC_Results.

# Result folder
results_folder <- 'QC_Results'

Define abbreviations for input files

To avoid complex and long file names we can use an abbreviated/simplified name for each of the files used as input. For example, PROJ1_Cohort3_Model1_date_v2 will be treated as PROJ1_Cohort3_A1, or PROJ1_Cohort2_Plate_ModelA1_20170309 as PROJ1_Cohort2_A2. The length of the prefix vector must be equal to the number of files used as input:

# Prefixes for each file
prefixes <- c('PROJ1_Cohort3_A1', 'PROJ1_Cohort3_A2',
              'PROJ1_Cohort2_A1','PROJ1_Cohort2_A2', 
              'Cohort1_A1', 'Cohort1_A2')

Illumina Array type and filter conditions

We need to indicate the type of Illumina array used to measure DNA methylation in each of the EWAS results files that we are QCing. Two different array types are possible: 450K or EPIC. They should be listed following the same order as in the character vector file or prefixes (see example). Filtering of CpGs will depend on the Illumina array, thus this field has to be completed. 

# Array type, used : EPIC or 450K
artype <- c('450K', 'EPIC', '450K', 'EPIC', '450K', 'EPIC')

Module 1 - Quality Control of EWAS results, allows the exclusion of CpG probes that do not accomplish certain criteria. The criteria to designate "problematic" CpG probes are based on [@zhou2017comprehensive] (– genome build GRCh38), [@solomon2018comparison], [@fernandez2019comparison], and can be defined by the user in a character vector as indicated in the example below:

Ethnicity

We can define the ethnic origin of the study population for each of the EWAS results files used as input in Module 1 - Quality Control module. . Ethnic origin can be indicated according to the code from the table below. If the population is very diverse then select GMAF1p.

In specific MASK_snp5_ethnic variable we define exclusion whether 5bp 3'-subsequence (including extension for typeII) overlap with any of the SNPs with global MAF >1\% for each ethnicity.

\

In ethnic variable we define the ethnic for each cohort.

\

| Population Code | Population Description | Super Population Code | | :-------------------: | :---------------------------------------------------------------------- | :-------: | | AFR | African | AFR | | AMR | Ad Mixed American | AMR | | EAS | East Asian | EAS | | EUR | European | EUR | | SAS | South Asian | SAS | | CHBB | Han Chinese in Beijing, China | EAS | | JPT | Japanese in Tokyo, Japan | EAS | | CHS | Southern Han Chinese | EAS | | CDX | Chinese Dai in Xishuangbanna, China | EAS | | KHV | Kinh in Ho Chi Minh City, Vietnam | EAS | | CEU | Utah Residents (CEPH) with Northern and Western European Ancestry | EUR | | TSI | Toscani in Italia | EUR | | FIN | Finnish in Finland | EUR | | GBR | British in England and Scotland | EUR | | IBS | Iberian Population in Spain | EUR | | YRI | Yoruba in Ibadan, Nigeria | AFR | | LWK | Luhya in Webuye, Kenya | AFR | | GWD | Gambian in Western Divisions in the Gambia | AFR | | MSL | Mende in Sierra Leone | AFR | | ESN | Esan in Nigeria | AFR | | ASW | Americans of African Ancestry in SW USA | AFR | | ACBB | African Caribbeans in Barbados | AFR | | MXL | Mexican Ancestry from Los Angeles USA | AMR | | PUR | Puerto Ricans from Puerto Rico | AMR | | CLM | Colombians from Medellin, Colombia | AMR | | PEL | Peruvians from Lima, Peru | AMR | | GIH | Gujarati Indian from Houston, Texas | SAS | | PJL | Punjabi from Lahore, Pakistan | SAS | | BEBB | Bengali from Bangladesh | SAS | | STU | Sri Lankan Tamil from the UK | SAS | | ITU | Indian Telugu from the UK | SAS | | | | | | GMAF1p | If population is very diverse | |

# Exclude - MASK snp5
ethnic <- c('EUR', 'EUR', 'SAS', 'SAS', 'EUR', 'EUR', 'GMAF1p')

Perform CpG exclusions –> control and non-CpG probes, and probes targeting CpGs in sexual chromosomes:

Perform CpG exclusions –> probes with hybridizing problems

Perform CpG exclusions -> probes affected by the presence of SNPs

Perform CpG exclusions -> probes giving inconsistent results between arrays

In this example we filter CpGs that meet the following conditions: MASK_sub30_copy, MASK_extBase, MASK_mapping, MASK_typeINextBaseSwitch, control_probes, Unrel_450_EPIC_blood and Sex. This is the recommended configuration for studies conducted in blood from European ancestry individuals using the 450K array

# Parameters to exclude CpGs
exclude <-  c( 'control_probes',
               'noncpg_probes',
               'Sex',
               'MASK_mapping', 
               'MASK_sub30_copy', 
               'MASK_extBase', 
               'MASK_typeINextBaseSwitch', 
               'MASK_snp5_ethnic',
               'Unrel_450_EPIC_blood' )

Other variables :

To create a precision plot and also to conduct the EWAS meta-analysis we need to indicate the number of samples included in each of the EWAS results files. We indicate this information in a vector called N. In addition, for case-control EWAS, we need to know the sample size of exposed or diseased individuals in each of the EWAS results files. This information is indicated in a vector called n.

N <- c(100, 100, 166, 166, 240, 240 )
n <- c(NA)

Overview of the QualityControl.R code

r colorize("Note: all this part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify or to adapt it to their needs.", 'red')

This code will be executed for each EWAS results file defined in the variable files. . However, in this example we only show the analysis workflow for one study (one cohort and model). The complete code can be found in QualityControl.R .

# Variable declaration to perform precision plot
medianSE <- numeric(length(files))
value_N <- numeric(length(files))
cohort_label <- character(length(files))

# Prepare output folder for results (create if not exists)
if(!dir.exists(file.path(getwd(), results_folder )))
   suppressWarnings(dir.create(file.path(getwd(), results_folder)))


# IMPORTANT FOR A REAL ANALYSIS :

# To show the execution flow we perform the analysis with only one data
# file. Normally, we have more than one data file to analyze, for that
# reason, we execute the code inside a loop and we follow the execution
# flow for each file defined in `files` 
# So we need to uncomment the for instruction and remove i <- 1 assignment.

# for ( i in 1:length(files) )
# {

   # we force i <- 1 to execute the analysis only for the first variable
   # for real data we have to remove this line
   i <- 1

   # Prepare output subfolder for cohort-model results (create if not exists)
   if(!dir.exists(file.path(getwd(), results_folder, prefixes[i] )))
      suppressWarnings(dir.create(file.path(getwd(), results_folder, prefixes[i])))

   # Creates an empty file to resume all data if an old file exist  removes
   # the file and creates a new one
   fResumeName <- paste0( file.path(getwd(), results_folder, prefixes[i]),
                          "/",prefixes[i], "_descriptives.txt")
   if ( file.exists(fResumeName) ) {
      file.remove(fResumeName)
   }
   file.create(fResumeName)

   # Read data.
   cohort <- read.table(files[i], header = TRUE, as.is = TRUE)
   print(paste0("Cohort file : ",files[i]," - readed OK", sep = " "))

First, the code reads the content of the file with EWAS results,

# Read data.
cohort <- read.table(files[i], header = TRUE, as.is = TRUE)
print(paste0("Cohort file : ",files[i]," - readed OK", sep = " "))

Then, it stores the content in a variable named cohort.

After that, the code executes the steps fo the quality control (Figure\@ref(fig:qcworkflow)). These are the functions that are used:

  1. clean_NA_from_data function: This function remove the rows (CpGs) with NA in any of the variables and return only complete cases.
# Remove rows with NA from data
cohort <- clean_NA_from_data(cohort)
  1. descriptives_CpGs function: This function performs a descriptive analysis of main variables ( Beta, SE and P-value) in the EWAS results file (QC_Results/PROJ1_Cohort3_A1_descriptives.tx). 
# Descriptives - Before CpGs deletion #
descriptives_CpGs(cohort, c("BETA", "SE", "P_VAL"), fResumeName, before = TRUE)
  1. test_duplicate_CpGs function: This function tests whether there are any duplicated CpGs in the EWAS results file. If there are, the script execution stops and writes the duplicates and descriptive related to these duplicates in a file.
# Remove duplicates
test_duplicate_CpGs(cohort, "probeID", paste0(results_folder,'/',prefixes[i],'_duplicates.txt') )
  1. filterLowRepresentedCpGsinCohort function: This function removes low represented CpGs in a given cohort and model. Low represented CpGs are defined as those CpGs not assessed in a given % of the subjects of the study as indicated in the pcMissingSamples (by default 0.9) . This data is reported in descriptive resume. To apply this filtering, the EWAS results file has to have a column named as defined in colname_NforProbe parameter, ('N_for_probe'). This data is updated in descriptive resume.
# Remove cpGs with low representation
   # first, we test if colname_NforProbe and pcMissingSampes are defined
   if( !exists("colname_NforProbe") ) { colname_NforProbe <- NULL }
   if( !exists("pcMissingSamples") ) { pcMissingSamples <- NULL }

# Remove cpGs with low representation
cohort <- filterLowRepresentedCpGsinCohort(cohort, 
                                           colname_NforProbe, 
                                           pcMissingSamples, N[i], 
                                           fileresume = fResumeName )
  1. exclude_CpGs function: This function excludes the list of CpGs that the user has selected (section 4.2.4). The function uses the parameters defined before in the exclude variable ,which are the data, cohort, the CpG id field (can be the column number or field name "probeId"), ”), the filters to apply, and, optionally, a file name where to save excluded CpGs and the exclusion reason (in this case the file name would be QC_Results/PROJ1_Cohort3_A1_excluded.txt).

We only take in to account ethnic group if MASK_snp5_ethnic is defined in exclude variable.

\ 

# Exclude CpGs not meet conditions. 
    if("MASK_snp5_ethnic" %in% exclude ){
       cohort <- exclude_CpGs(cohort, "probeID", 
                              exclude, 
                              ethnic = ethnic[i], 
                              filename = paste0(results_folder, '/',prefixes[i], 
                                                '/',prefixes[i],'_excluded.txt'), 
                              fileresume = fResumeName, artype = artype[i] )
    } else {
       cohort <- exclude_CpGs(cohort, "probeID", exclude, 
                              ethnic = "", 
                              filename = paste0(results_folder, '/',prefixes[i], 
                                                '/',prefixes[i],'_excluded.txt'), 
                              fileresume = fResumeName, 
                              artype = artype[i] )
    }

\

  1. descriptives_CpGs function (bis): After eliminating CpGs, we proceed to carry out another descriptive analysis with the same function as above.At the end of each iteration we get a file named xxx with descriptive before and after quality control, the number of excluded CpGs, and the number of statistically significant CpGs at different p-value thresholds: nominal and after multiple testing correction with the False Discovery Rate (FDR) and Bonferroni methods.

\ 

# Descriptives - After CpGs deletion #
 descriptives_CpGs(cohort, c("BETA", "SE", "P_VAL"), 
                   fResumeName, before = FALSE )

\

  1. adjust_data function: This function obtains the adjusted p-values after multiple testing correction applying the Bonferroni and FDR methods. We need to indicate in which column the p-value is found and what adjustment we want. By default, the function adjusts data by Bonferroni (bn) and FDR (fdr).

This function returns the input data adding two new columns corresponding to adjusted Bonferroni and FDR p-values. This function returns the input data adding two new columns corresponding to these adjustments. As in other functions seen before, optionally, we can get a data summary with the number of statistically significant p-values by Bonferroni or FDR, in a text file (the generated file in the example is called QC_Results/PROJ1_Cohort3_A1_ResumeSignificatives.txt ).

\ 

# data before adjustment
head(cohort)

# Adjust data by Bonferroni and FDR
# Adjust data by Bonferroni and FDR
   cohort <- adjust_data(cohort, "P_VAL", 
                         bn=TRUE, 
                         fdr=TRUE, fResumeName, N[i] )

# data after adjustment
head(cohort)

\

  1. write_QCData function: TThis function annotates the QCed EWAS results using the the Illumina annotations for 450K or EPIC arrays (as indicated by the user) and saves it. The file generated by this function is the input for Module 2 - Meta-analysis of EWAS results. This data is stored with the _QC_Data.txt sufix.

\ 

# Write QC complete data to external file
write_QCData(cohort, paste0(results_folder, '/',prefixes[i], '/',prefixes[i]))

\

Quality Control - code for plots

\

The quality control process is summarized in different plots.

\

  1. plot_distribution function: It generates, for each cohort and model, several plots: a plot with the distribution of SEs, a plot with the distribritution of p-values, and a QQ plot of expected versus observerd p-values.

\

```rSE distribution plot", fig.pos='ht'} ## Visualization - Plots

# Distribution plot plot_distribution(cohort$SE, main = paste('Standard Errors of', prefixes[i]), xlab = 'SE')

\ 

```rp-value distribution plot", fig.pos='ht'}
   ## Visualization - Plots

   plot_distribution(cohort$P_VAL, 
                     main = paste('p-values of', prefixes[i]), 
                     xlab = 'p-value')

\

```rQQ-plots", fig.pos='ht'} # QQ-plot. qqman::qq(cohort$P_VAL, main = sprintf('QQ-plot of %s (lambda = %f)', prefixes[i], lambda = get_lambda(cohort,"P_VAL")))

\ 

2.  `plot_volcano` function: It generates a Volcano plot for each cohort and model.

\ 

```rVolcano Plot", fig.pos='ht'}
   # Volcano plot.
   plot_volcano(cohort, "BETA", "P_VAL", 
                main=paste('Volcano plot of', prefixes[i]) )

\

  1. plot_precisionp function:It performs a precision plot to compare SE (precision) vs study sample size (or sample size of cases) across cohorts and models.

\ 

plot_precisionp(precplot.data.n, 
                paste(results_folder,  "precision_SE_N.png", sep='/'), 
                main = "Subgroup Precision Plot -  1/median(SE) vs sqrt(n)")

\

```rPrecision plot for 7 different datasets ", fig.pos='ht'} include_graphics("imgs/QC/precision_SE_N.png") 

\ 

4.  `plot_betas_boxplot` function: It performs a box plot of Betas across cohorts and models

\ 

```r
plot_betas_boxplot(betas.data, 
                   paste(results_folder, 'BETAS_BoxPlot.pdf', sep="/"))

\

```rBetas Boxplot plot for 10 different datasets ", fig.pos='ht'} include_graphics("imgs/QC/BETAS_BoxPlot.png") 

\ 

5.  `plot_venndiagram` function: We need to define the venn diagram for a maximum of 5 datasets. Here we define which models and cohorts we want to be shown in the Venn diagram. In this example we define two different Venn diagrams, one with  "Cohort1_A1", "PROJ1_Cohort2_A1", "PROJ1_Cohort2_B1", "PROJ1_Cohort2_C1" and "PROJ1_Cohort3_A1" datasets and the other with five more datasets "Cohort1_A2", "PROJ1_Cohort2_A2", "PROJ1_Cohort2_B2", "PROJ1_Cohort2_C2" and "P1_Cohort3_A2". We can plot VennDiagram with adjusted data by Bonferroni or FDR.

```r
# Initiall we had defined the venn_diagrams variable to configure each venn diagram 
# Venn diagrams
venn_diagrams <- list(
   c("Cohort1_A1", "PROJ1_Cohort2_A1", "PROJ1_Cohort2_B1", "PROJ1_Cohort2_C1", "PROJ1_Cohort3_A1" ),
   c("Cohort1_A2", "PROJ1_Cohort2_A2", "PROJ1_Cohort2_B2", "PROJ1_Cohort2_C2", "P1_Cohort3_A2" )
)

# Venn_Diagrams()
   for (i in 1:length(venn_diagrams))
      plot_venndiagram(venn_diagrams[[i]], qcpath = results_folder, plotpath = results_folder, bn='padj.bonf', fdr='padj.fdr')

```rVenn Diagram ", fig.pos='ht'} include_graphics("imgs/QC/venn_diagramm.png") 

# Meta-Analysis with GWAMA

## Meta-Analysis flowchart

To perform the meta-analyses, we use GWAMA, a Software tool for meta-analysis developed by the Institute of Genomics from University of Tartu. This software is available at [https://genomics.ut.ee/en/tools/gwama-download](https://genomics.ut.ee/en/tools/gwama-download).

Like Module 1 of the **EASIER R package**, we have created a script named **MetaAnalysis.R** to carry out the Module 2 - Meta-analysis of EWAS results (workflow in Figure Figure\@ref(fig:metaworkflow)). This script needs some minor editing to indicate information specific to the study. Briefly, it starts formatting the QCed EWAS results and preparing configuration files to run GWAMA. Then, it runs the inverse-variance weighted meta-analysis (fixed- and random-effects are tested), annotates the results and performs some quality control checks (lamda, QQ plot, heterogeneity ($I^2$), etc). Finally, it performs forest plots of the top CpGs and Venn diagrams to compare results between meta-analyses



```rMeta-analysis flowchart. The script MetaAnalysis.R, contains the code to perform the steps indicated in the flowchart.The only editing required by the researcher is defining the initial variables, which are specific to each study. The rest of the script is automatic and does not need any editing.", fig.pos='ht'}
include_graphics("imgs/workflows/MetaAnalysisWorkflow.png")

Initial definition of variables specific to the study

Like in Module 1, in Module 2 - Meta-analysis of EWAS results, we need to define some initial parameters, which are specific to each study. r colorize("(this is the only part of the code that requires editing!)", 'red'). These variables are:

  1. artype: Type of array, either EPIC or 450K. If you are combining studies with data from the EPIC array and studies with data from the 450K array, remember to run Module 1 - Quality control of EWAS results selecting '450K' if you only want to consider CpG probes from the 450K array, or selecting 'EPIC' if you want to consider all the CpG probes from EPIC even though for some cohorts you will have more than 50\% missing's.

  2. metafiles: List of files with QCed EWAS results that will be combined and analysed in each meta-analysis model. In the example below we have defined two different meta-analysis models, MetaA1 and MetaA2. In the first one, MetaA1, we will meta-analyse EWAS results from 'PROJ1_Cohort3_A1', 'PROJ1_Cohort2_A1' and 'Cohort1_A1'.

  3. pcentMissing: We can also exclude from the meta-analysis those CpGs not present in a % of the studies, by setting the minimum representation percentage. By default, we have set up the percentage to 80%. This means the code will execute the meta-analysis twice, one with all CpGs and another with only CpGs present in 80% of the studies. Note: If you are combining data from 450K and EPIC arrays and you selected 'EPIC' as the artype, with this setting you are able to choose running a meta-analysis in parallel only considering CpGs with high representation and therefore eliminating those CpGs only present in the EPIC array.

  4. results_folder: To indicate the folder where to save the output of the meta-analysis, we recommend to set it to “QC_Results”.

  5. venndiag_threshold: To indicate the p-value threshold to consider CpGs as statistically significant (by default p-value <0.05).

  6. venn_diagrams: To compare the overlap of significant CpGs between meta-analysis. P-value significance threshold is configured in venndiag_threshold variable, by default this value is 0.05. In the example we are comparing the results of meta-analysis 1 (MetaA1) and meta-analysis 2 (MetaA2). .

  7. gwama.dir: To define the GWAMA execution path.

\ 

## -- Variable definition for Meta-Analysis -- ##

# Array type, used : EPIC or 450K
artype <- '450K'

# Define data for each meta-analysis
metafiles <- list(
   'MetaA1' = c('Cohort1_A1','PROJ1_Cohort2_A1', 'PROJ1_Cohort3_A1' ),
   'MetaA2' = c('Cohort1_A2','PROJ1_Cohort2_A2', 'PROJ1_Cohort3_A2' ))

# Define maximum percent missing for each CpG
pcentMissing <- 0.8 # CpGs with precense lower than pcentMissing after EWAS
                    # meta-analysis will be deleted from the study.

# Paths with QCResults and path to store GWAMA results
results_folder <- 'QC_Results'
results_gwama <- '.'

# Venn diagrams ==> IMPORTANT : maximum 5 meta-analysis by venn diagram
venndiag_threshold <- 0.05

venn_diagrams <- list(
   c("MetaA1", "MetaA2" )
)

## -- End Variable definition for Meta-Analysis -- ##

# GWAMA binary path  (GWAMA IsGlobal Server sw05 and sw06 installation)
gwama.dir <- paste0(Sys.getenv("HOME"), 
                    "/data/EWAS_metaanalysis/1_QC_results_cohorts/GWAMA/")

Overview of the MetaAnalysis.R code

Note: all this part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify it to adapt to their needs.

The meta-analysis is performed twice: first with all CpGs; and second with CpGs present in x% of the studies (as indicated in the pcentMissing variable). In this example, however, we only run the first meta-analysis but in a full run script both meta-analyses are performed.

First, the code creates the needed folders. In this example we create a GWAMA folder where we will put the input files for GWAMA, and a GWAMA_Results folder where we will store the results. When we finish the code execution the GWAMA folder with temporal configuration files will be removed.

\ 

## Create directory for GWAMA configuration files and GWAMA_Results 
## inside the defined results_gwama variable defined before.
if(!dir.exists(file.path(getwd(), 
                         paste(results_gwama, "GWAMA", sep="/") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste(results_gwama, "GWAMA", sep="/"))))

## Create directory for GWAMA_Results
outputfolder <- paste0(results_gwama, "/GWAMA_Results")
if(!dir.exists(file.path(getwd(), outputfolder )))
   suppressWarnings(dir.create(file.path(getwd(), outputfolder)))

# We create a map file for GWAMA --> Used in Manhattan plots.
# We only need to indicate the array type
hapmapfile <- paste(results_gwama,"GWAMA", "hapmap.map" ,sep = "/")
generate_hapmap_file(artype, hapmapfile)

We also create some subfolders to store configuration files inside the GWAMA folder, one subfolder with configuration files for each meta-analysis model, for example, for MetaA1 meta-analysis we create the path GWAMA\MetaA1.

\ 

list.lowCpGs <- NULL

# Create folder for a meta-analysis in GWAMA folder, here we 
# store the GWAMA input files for each meta-analysis,
# We create one for complete meta-analysis
if(!dir.exists(file.path(getwd(), 
                         paste(results_gwama,"GWAMA", names(metafiles)[metf],
                               sep="/") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste(results_gwama,"GWAMA", 
                                               names(metafiles)[metf], 
                                               sep="/"))))
# We create another for meta-analysis without filtered CpGs with low 
# percentage (sufix _Filtr)
if(!dir.exists(file.path(getwd(), 
                         paste0(results_gwama,"/GWAMA/", 
                                names(metafiles)[metf],
                                "_Filtr") )))
   suppressWarnings(dir.create(file.path(getwd(), 
                                         paste0(results_gwama, "/GWAMA/",
                                                names(metafiles)[metf],
                                                "_Filtr"))))

# GWAMA File name base
inputfolder <- paste0(results_gwama,"/GWAMA/",  names(metafiles)[metf])

modelfiles <- unlist(metafiles[metf])

# Execution with all CpGs and without filtered CpGs
runs <- c('Normal', 'lowcpgs') 
lowCpGs = FALSE;
outputfiles <- list()

outputgwama <- paste(outputfolder,names(metafiles)[metf],sep = '/')

\pagebreak

Now we are ready to execute the meta-analysis of QCed EWAS results following the steps indicated in the workflow MetaAnalysis.R. These are the functions that will be used:

\

  1. create_GWAMA_files:

\

To modify EWAS QCed results to GWAMA format and create GWAMA configuration files. This function specifies

\ 

 if(runs[j]=='lowcpgs') {
   lowCpGs = TRUE
   # Get low presence CpGs in order to exclude this from the new meta-analysis
   list.lowCpGs <- get_low_presence_CpGs(outputfiles[[j-1]], pcentMissing)
   inputfolder <- paste0(results_gwama,"/GWAMA/",  names(metafiles)[metf], "_Filtr")
   outputgwama <- paste0(outputgwama,"_Filtr")
 }

 # Create a GWAMA files for each file in meta-analysis and one file with 
 # gwama meta-analysis configuration
 for ( i in 1:length(modelfiles) )
   create_GWAMA_files(results_folder,  modelfiles[i], 
                      inputfolder, N[i], list.lowCpGs )

\

  1. run_GWAMA_MetaAnalysis

\

To run both fixed-effects and random-effects inverse-variance weighted meta-analsyis with GWAMA. This function needs

All these parameters have been specified in section 5.2 Initial definition of variables specific to the study.

\ 

 # Execute GWAMA meta-analysis and manhattan-plot, QQ-plot and a file 
 # with gwama results.
 outputfiles[[runs[j]]] <- run_GWAMA_MetaAnalysis(inputfolder, 
                                                 outputgwama, 
                                                 names(metafiles)[metf], 
                                                 gwama.dir)

```rManhattan plot obtained with GWAMA ", fig.pos='ht'} include_graphics("imgs/meta/manhattan.png") 

\ 

3.  **`get_descriptives_postGWAMA`**: 

To make a quality control check of the meta-analyses results. This function is similar to what was done in Module 1 - **Quality control of EWAS results**, but instead of being applied to the cohort specific EWAS results, it is applied to the meta-analysis EWAS results. This function does:
-   calcutes adjusted p-values (FDR and Bonferroni),
-   annotate CpGs with Illumina (hg19 (GRCh37)),
-   generates a file with a summary of the meta-analysis results, 
-   generates plots showing the distribution of the heterogeneity, SE, and p-values,
-   generates QQ-plots with lambda, Manhattan plots and Volcano-plots.

\ 

```r
 # Post-metha-analysis QC --- >>> adds BN and FDR adjustment
 dataPost <- get_descriptives_postGWAMA(outputgwama, 
                                       outputfiles[[runs[j]]], 
                                       modelfiles, 
                                       names(metafiles)[metf], 
                                       artype, 
                                       N[which(prefixes %in% modelfiles)] )

```rHeterogeneity distribution plot (i2) ", fig.pos='ht'} include_graphics("imgs/Heteroi2.png") 

\ 

4.  **`plot_ForestPlot`**: 

\ 

To generate forest plots for the top 30 statistically significant CpGs of the fixed- and random-effects models, 

\ 

```r
 # Forest-Plot
 plot_ForestPlot( dataPost, metafiles[[metf]], runs[j], 
                 inputfolder, names(metafiles)[metf], files, outputgwama  )

```rForest plot for cpg22718050 ", fig.pos='ht'} include_graphics("imgs/FP_cg22718050.png") 

\pagebreak

\ 

5. **`venn_diagrams`**: 

\ 

To generate Venn diagrams comparing the overlap of statistcially significant CpGs between meta-analysis models. CpGs with FDR or BN corrected p-values can be selected for this comparison.

\ 

```r
for (i in 1:length(venn_diagrams))
   plot_venndiagram(venn_diagrams[[i]], qcpath = outputfolder, 
                    plotpath =  paste0(results_gwama, "/GWAMA_Results"), 
                    pattern = '_Fixed_Modif.out',bn='Bonferroni', fdr='FDR',
                    venndiag_threshold)

\

this is venn diagram output example

```r Venn diagram example", fig.pos='ht'} include_graphics("imgs/meta/venn.png") 

# Enrichment

## Enrichment Flowchart

Like the other EASIER modules, for Module 3 –  **Functional enrichment of EWAS results**, we have created a script named **Enrichment.R**. This script needs some minor editing to indicate information specific to the study


\ 

The script follows the workflow indicated in **Figure\@ref(fig:enrichworkflow)**. Briefly, the first step the one that needs editing by the user, is to define initial
parameter specific to the study. Then we read the files with the list of CpGs for which we want to test for the enrichment. If the file only contains CpG names, we start annotating these CpGs to genes and to other molecular features. After that, the script performs gene-set enrichment (at the gene level), by applying missmethyl R package using the databases: GO terms, KEGG and Molecular Signatures DB (MSigDB) and enrichment with ConsensusPathDB using brgeEnrich package [https://github.com/isglobal-brge/brgeEnrich](https://github.com/isglobal-brge/brgeEnrich). Then, it performs enrichment for gene and CpG island relative positions. In this case, the enrichment is performed for all CpGs as well as for hypo and hyper-methylated CpGs, given that coefficients of the association are provided in the list of CpGs. Finally it performs molecular enrichment specific for these tissues: blood, placenta or other. For blood, it tests the enrichment for blood chromatin states. For placenta, it test the enrichment for placenta chromatin states, for placenta partially methylated domains (PMDs) [@schroeder2013has] and for placenta imprinted regions [@hamada2016allele].

\ 

```rFunctional enrichment flowchart. This flowchart is used in the script under test folder to perform the enrichment (Enrichment.R). The most important step in this workflow is the first step where we have to define the variables, if variables are well defined all the process is 'automatic' ", fig.pos='ht'}
include_graphics("imgs/workflows/EnrichmentWorkflow.png")

\

Initial definition of variables specific to the study

\

Like in the other modules of the EASIER R package, in Module 3 - Functional Enrichment if EWAS results we need to define some initial variables, which are specific to the study (r colorize('this is the only part of the code that has to be edited!', 'red')).

These variables are:

\

1. Working directory:

\

First, we need to set up the working directory. By default, we set the working directory to the meta-analysis folder.

\ 

# Set working directory to metaanalysis folder
setwd("<path to metaanalysis folder>/metaanalysis")

\

2. Files to enrich:

\

To indicate directory and files with the CpGs to be used for the enrichment. We need to indicate the path to the files with the list of CpGs to be tested for the functional enrichment (FilesToEnrich variable). These files can contain, either a list of CpG names or the results from Module 2 - Meta-analysis of EWAS results including the associated p-values and Betas together with the CpG names

\ 

# Files with CpG data to enrich may be a CpGs list or annotated GWAMA output
FilesToEnrich <- c('toenrich/CpGstoEnrich.txt',
                   'GWAMA_Results/MetaA1/MetaA1_Fixed_Modif.out'
                    )

\

3. P-value threshold:

\

In the case of performing the functional enrichment using the results of meta-analysis results, we need to define the p-value threshold to select the CpGs to test for the enrichment. One of these options is possible:

\ 

# Values for adjustment
BN <-  TRUE       # Use Bonferroni ?
FDR <- 0.05        # significance level for adjustment, if NA FDR is not used
pvalue <- 0.05    # significance level for p-value, if NA p-value is not used

\

4. Array type:

\

artype, type of array: 450K or EPIC.

\ 

# Array type, used : EPIC or 450K
artype <- '450K'

\

5. Indicate folders for input and output data:

\

We have to indicate the folders with the CpGs to enrich (either the meta-analysis results from EASIER module 2 or a file with a list of CpG names), and the folder to store results fo the functional enrichment analyses.

\ 

# Result paths definition for QC, Meta-Analysis and Enrichment
results_folder <- 'QC_Results'
results_gwama <- '.'
results_enrich <- 'Enrichment'

\

6. Enrichment type:

\

To define the type of enrichment to be performed (enrichtype variabe). Two options are possible:

\ 

# Enrichment type :  'BLOOD' or 'PLACENTA'
#     if enrichtype <- 'BLOOD' => enrichment with : 
#                          Cromatine States : BLOOD (crom15)
#                          (To be implemented in future) Partially Methylated Domains (PMD) for Blood
#     if enrichtype <- 'PLACENTA' => enrichment with: 
#                          Cromatine States : PLACENTA (FP_15) optionally (FP_18)
#                          Partially Methylated Domains (PMD) for Placenta
#     if enrichtype is different from 'BLOOD' and 'PLACENTA' 
#     we only get the missMethyl and MSigDB enrichment and the Unique genes list.
enrichtype <- 'PLACENTA'

# Cromatine States Placenta Enrichment FP_18
# if enrichFP18 = TRUE the enrichment is performed wit FP_15 and FP_18
enrichFP18 <- FALSE

\

7. Enrichment statistical test:

\

To indicate which statistical test we want to apply for the enrichment we define the variable testdata. It can be Fisher or Hypergeometric (by default: Fisher).

\ 

# Test to be used: 'Fisher' or 'Hypergeometric' for different values no test will be performed
testdata <- 'Fisher'

\

Then the code creates several folders where to store the results. r colorize('No editing is needed in this part.', 'red').

\ 

## Check if we have any files to enrich and if these files exists
if (length(FilesToEnrich)>=1 & FilesToEnrich[1]!='') {
   for ( i in 1:length(FilesToEnrich))
      if (!file.exists(FilesToEnrich[i])) {
         stop(paste0('File ',FilesToEnrich[i],' does not exsits, please check file' ))
      }
}

## Check variables

if( ! toupper(enrichtype) %in% c('PLACENTA','BLOOD') )
   warning('Only enrichment with MyssMethyl and MSigDB will be done')

if( ! tolower(testdata) %in% c('fisher','hypergeometric') )
   warning('Wrong value for testdata variable, values must be "Fisher" or "Hypergeometric". No test will be performed ')



# Convert relative paths to absolute paths for FilesToEnrich
FilesToEnrich <- unlist(sapply(FilesToEnrich, 
                               function(file) { if(substr(file,1,1)!='.' & substr(file,1,1)!='/') 
                                  file <- paste0('./',file) 
                               else 
                                  file }))
FilesToEnrich <- sapply(FilesToEnrich, file_path_as_absolute)

if(results_enrich!='.'){
   outputfolder <- file.path(getwd(), results_enrich )
}else{
   outputfolder <- file.path(getwd() )}


# Create dir to put results from enrichment
if(!dir.exists(outputfolder))
   suppressWarnings(dir.create(outputfolder))

setwd( outputfolder)

\

Overview of the Enrichment.R code

r colorize('Note: This part of the code can be run without any editing from the researcher! We present it in case some researchers want to modify or adapt it to their needs.', 'red')

\

```rFunctional enrichment flowchart. Detailed common enrichment for blood, placenta and other", fig.pos='ht'} include_graphics("imgs/enrich/common.png") 

\ 

### Read list of CpGs for enrichment 

\ 

The procedure that we will detail here is executed for each one of the files entered in the variable `FilesToEnrich.` We show how it works with a list of CpG names (first file in `FilesToEnrich` variable) and with meta-analysis output results  (second file in `FilesToEnrich` variable). 

\ 

```r
i <- 1 # We get first file in FilesToEnrich

# Enrich all CpGs
allCpGs <- FALSE

# Get data
data <- NULL
data <- read.table(FilesToEnrich[i], header = TRUE, 
                   sep = "", dec = ".", stringsAsFactors = FALSE)

\

Annotate CpGs (if working with lists of CpG names)

After defining the variables specific to each study, we read the file content and test if data is a list of CpG names or an output file from Module 2 – Meta-analysis of EWAS results. If the input file is a list of CpG names we first annotate it with the get_annotations function (meta-analysis results files are already annotated). In get_annotattions we need to define the artype (array type) parameter , and the data to be annotated.

\

Annotation is done using Illumina (hg19 (GRCh37))

\ 

if(dim(data)[1] <= 1 | dim(data)[2] <= 1) {
   data <- read.table(FilesToEnrich[i], dec = ".") # Avoid header
   data <- as.vector(t(data))
   head(data)
   data <- get_annotattions(data, artype, FilesToEnrich[i], outputfolder )

   head(data)

   allCpGs <- TRUE
   data$chromosome <- substr(data$chr,4,length(data$chr))
   data$rs_number <- data$CpGs
}

\

Get list of unique genes

\

The getUniqueGenes function retrieves the list of unique genes annotated to the significant CpGs and stores the result in geneUniv variable. This list of genes will be used to perform the gene-set enrichment analyses.

\ 

      # get unique genes from data
geneUniv <- lapply( lapply(miss_enrich[grepl("signif", names(miss_enrich))], 
                           function(cpgs) { 
                              data[which(as.character(data$CpGs) %in% cpgs),]$UCSC_RefGene_Name
                              }),
                    getUniqueGenes)

geneUniv

\

Gene-set enrichment: GO terms and KEGG

\

The gene-set enrichment analysis is conducted with the missMethyl_enrichment function (which is based on the missmethyl R package [@missmeth1], [@missmeth2], [@missmeth3], [@missmeth4]). missmethyl performs the enrichment by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. It uses two reference databases:

If the list of CpGs does not contain p-values, all the CpGs will be used for the enrichment. If the list of CpGs contains p-values, we will use those passing a specific p-value threshold (indicated in the section 6.2).

\ 

## -- Functional Enrichmnet
## ------------------------

# Enrichment with missMethyl - GO and KEGG --> Writes results to outputfolder
miss_enrich <- missMethyl_enrichment(data, outputfolder, FilesToEnrich[i], 
                                     artype, BN, FDR, pvalue, allCpGs, plots = TRUE )

head(miss_enrich$GO)
head(miss_enrich$KEGG)

\

Gene-set enrichment: Molecular Signatures Database (MSigDB)

\

MSigDB_enrichment function conducts a functional enrichment using missmethyl R package and the public database Molecular Signatures Database (MSigDB). The same parameters as above have to be indicated

\ 

## -- Molecular Enrichmnet
## -----------------------

# Molecular Signatures Database enrichment
msd_enrich <- MSigDB_enrichment(data, outputfolder, FilesToEnrich[i], artype, BN, FDR, pvalue, allCpGs)

head(msd_enrich$MSigDB)

\

Gene-set enrichment: ConsensusPathDB

\

get_consensusPdb_OverRepresentation function conducts a functional enrichment using brgEnrich R package and the public database ConsensusPathDB Database (http://consensuspathdb.org). We can get enrichment for different FileSet types :

\

\ 

## -- Online Tools

# Enrichment with ConsensusPathDB
#     - Consensus path http://consensuspathdb.org/ 
#     (gene-set analysis – over-representation analysis)

# Available FSet types :
# 1 P     manually curated pathways from pathway databases
# 3 G2    Gene Ontology-based sets, GO level 2
# 4 G3    Gene Ontology-based sets, GO level 3
# 5 G4    Gene Ontology-based sets, GO level 4
# 6 G5    Gene Ontology-based sets, GO level 5

acFSet <- c('P', 'G2', 'G3')
acType <- 'entrez-gene'

# Get Enrichment
CPDB_enrich <- lapply(names(geneUniv), function( data, accFSet, genes ) {
   print(data)
   lapply(accFSet,
          get_consensusPdb_OverRepresentation,
          entityType='genes',
          accNumbers=na.omit(as.character(eval(parse(text = paste0("genes$",data))))),
          accType=acType,
          outputdir = "ConsensusPathDB",
          outputfile = gsub(".", "_", data, fixed=TRUE) )},
   accFSet = acFSet, genes = geneUniv)

names(CPDB_enrich) <- names(geneUniv)

Enrichment for gene relative positions

\

In this section we will test the enrichment of statistically significant CpGs for gene relative positions (annotated with the Illumina annotation as: 1st Exon, 3’UTR, 5’UTR, Gene Body, TSS1500 (distal promoter), TSS200 (proximal promoter)).

Given that we have provided the coefficients of the association, we can do the enrichment for all statistically significant CpGs and also stratifying by hypo- and hyper-methylated CpGs. This is done with the getHyperHypo function which classifies CpGs in significant or not (for FDR, BN or the indicated nominal p-value threshold) and in hypo- and hyper-methylated.

\ 

if("FDR" %in% colnames(data) & "Bonferroni" %in% colnames(data))
{

   ## -- Prepare data
   ## ---------------

   # Add column bFDR to data for that CpGs that accomplish with FDR
    # Classify fdr into "yes" and no taking into account FDR significance level
   data$bFDR <- getBinaryClassificationYesNo(data$FDR, "<", FDR) 

   # Classify by Hyper and Hypo methylated
   data$meth_state <- getHyperHypo(data$beta) # Classify methylation into Hyper and Hypo

   # CpGs FDR and Hyper and Hypo respectively
   FDR_Hyper <- ifelse(data$bFDR == 'yes' & 
                          data$meth_state=='Hyper', "yes", "no")
   FDR_Hypo <- ifelse(data$bFDR == 'yes' & 
                         data$meth_state=='Hypo', "yes", "no")

   # CpGs Bonferroni and Hyper and Hypo respectively
   BN_Hyper <- ifelse(data$Bonferroni == 'yes' & 
                         data$meth_state=='Hyper', "yes", "no")
   BN_Hypo <- ifelse(data$Bonferroni == 'yes' & 
                        data$meth_state=='Hypo', "yes", "no")

\

Then, we test the enrichment of the CpGs for gene relative positions with the getAllFisherTest (Fisher test) or the getAllHypergeometricTest (Hypergeometric test) functions. These functions write the results in the outputfile inside the previously indicated folder outputdir.

\ 

## --  CpG Gene position
## ---------------------

# Get descriptives
get_descriptives_GenePosition(data$UCSC_RefGene_Group, 
                              data$Bonferroni, 
                              "Bonferroni", 
                              outputdir = "GenePosition/Fisher_BN_Desc",
                              outputfile = FilesToEnrich[i])
get_descriptives_GenePosition(data$UCSC_RefGene_Group, d
                              ata$bFDR , "FDR", 
                              outputdir = "GenePosition/Fisher_FDR_Desc", 
                              outputfile = FilesToEnrich[i])


if( tolower(testdata) =='fisher') {
   ## --  Fisher Test - Gene position - FDR, FDR_hyper and FDR_hypo
   GenePosition_fdr <- getAllFisherTest(data$bFDR, 
                                  data$UCSC_RefGene_Group, 
                                  outputdir = "GenePosition/Fisher_FDR", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
   GenePosition_fdr_hyper <- getAllFisherTest(FDR_Hyper, 
                                  data$UCSC_RefGene_Group, 
                                  outputdir = "GenePosition/Fisher_FDRHyper", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
   GenePosition_fdr_hypo <- getAllFisherTest(FDR_Hypo, 
                                    data$UCSC_RefGene_Group, 
                                    outputdir = "GenePosition/Fisher_FDRHypo", 
                                    outputfile = FilesToEnrich[i], plots = TRUE )
}
else if ( tolower(testdata) =='hypergeometric') {
   ## --  HyperGeometric Test - Island relative position - 
   ## FDR, FDR_hyper and FDR_hypo (for Depletion and Enrichment)
   GenePosition_fdr <- getAllHypergeometricTest(data$bFDR, 
                                    data$UCSC_RefGene_Group, 
                                    outputdir = "GenePosition/HyperG_FDR", 
                                    outputfile = FilesToEnrich[i])
   GenePosition_fdr_hyper <- getAllHypergeometricTest(FDR_Hyper,
                                    data$UCSC_RefGene_Group,
                                    outputdir = "GenePosition/HyperG_FDRHyper", 
                                    outputfile = FilesToEnrich[i])
   GenePosition_fdr_hypo <- getAllHypergeometricTest(FDR_Hypo, 
                                   data$UCSC_RefGene_Group,
                                   outputdir = "GenePosition/HyperG_FDRHypo", 
                                   outputfile = FilesToEnrich[i])
}

\

Besides getting the results in a table, we also can get a plot with the odds ratios (ORs) of enrichment with the plot_TestResults_Collapsed function.

\ 

         plot_TestResults_Collapsed(list(fdr = GenePosition_fdr, 
                                         fdr_hypo = GenePosition_fdr_hypo, 
                                         fdr_hyper = GenePosition_fdr_hyper),
                                    outputdir = "GenePosition", 
                                    outputfile = FilesToEnrich[i], main = )

\

```rGene position with Fisher test for Hyper and Hypo methylated CpGs", fig.pos='ht'} include_graphics("imgs/enrich/plot_genepos.png") 

\ 

### Enrichment for CpG island relative positions

\ 

Similar as above, and with the function get_descriptives_RelativetoIsland we can get an enrichment for CpG island relative positions (CpG island, N-shore, N-shelf, S-shore, S-shelf). The function gives a plot together with the corresponding statistical results in a table.


\ 

```r

## --  CpG Island relative position
## --------------------------------

# Get descriptives
get_descriptives_RelativetoIsland(data$Relation_to_Island, 
                            data$Bonferroni, 
                            "Bonferroni", 
                            outputdir = "RelativeToIsland/Fisher_BN_RelativeToIsland", 
                            outputfile = FilesToEnrich[i])
get_descriptives_RelativetoIsland(data$Relation_to_Island, 
                            data$bFDR , 
                            "FDR", 
                            outputdir = "RelativeToIsland/Fisher_FDR_RelativeToIsland",
                            outputfile = FilesToEnrich[i])


if( tolower(testdata) =='fisher') {
   ## --  Fisher Test - Position Relative to Island - FDR, FDR_hyper and FDR_hypo
   relative_island_fdr <- getAllFisherTest(data$bFDR, 
                                  data$Relation_to_Island, 
                                  outputdir = "RelativeToIsland/Fisher_FDR", 
                                  outputfile = FilesToEnrich[i], plots = TRUE )
   relative_island_fdr_hyper <- getAllFisherTest(FDR_Hyper, 
                                  data$Relation_to_Island, 
                                  outputdir = "RelativeToIsland/Fisher_FDRHyper", 
                                  outputfile = FilesToEnrich[i], plots = TRUE )
   relative_island_fdr_hypo <- getAllFisherTest(FDR_Hypo,
                                       data$Relation_to_Island, 
                                       outputdir = "RelativeToIsland/Fisher_FDRHypo", 
                                       outputfile = FilesToEnrich[i], plots = TRUE )
}

\

```rGene position with Fisher test for Hyper and Hypo methylated CpGs", fig.pos='ht'} include_graphics("imgs/enrich/plot_relatisland.png") 

\ 

### Specific molecular enrichments for blood

\ 

As we show in **Figure \@ref(fig:enrichworkflowcommon)**,there are some functional enrichment analyses specific for each tissue. For blood, the code tests the enrichment for ROADMAP blood 15 chromatin states reference. To this end, each CpG in the array is annotated to one or several chromatin states by taking a state as present in that locus if it is described in at least 1 of the 27 blood-related cell types present in ROADMAP.


\ 

```rEnrichment flowchart. Detailed Blood enrichment", fig.pos='ht'}
include_graphics("imgs/enrich/blood.png")

\

Here, we also can select the Fisher or Hypergeometric test, and the enrichment is done for all CpGs and separately for hyper- and hypo-methylated CpGs (if Betas of the association are provided).

\ 

## --  ROADMAP  -  Metilation in Cromatine States - BLOOD
## -------------------------------------------------------
##  Analysis of methylation changes in the different chromatin 
##  states (CpGs are diff meth in some states and others don't)

# Prepare data
# Adds chromatine state columns
crom_data <- addCrom15Columns(data, "CpGId") 

if("FDR" %in% colnames(data) & "Bonferroni" %in% colnames(data))
{

   # Columns with chromatin status information :
   ChrStatCols <- c("TssA","TssAFlnk","TxFlnk","TxWk","Tx","EnhG",
                    "Enh","ZNF.Rpts","Het","TssBiv","BivFlnk",
                    "EnhBiv","ReprPC","ReprPCWk","Quies")

   if( !is.na(FDR) ) {
      chrom_states_fdr <- getAllChromStateOR( crom_data$bFDR, 
                                  crom_data[,ChrStatCols], 
                                  outputdir = "CromStates/OR_FDR", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
      chrom_states_fdr_hyper <- getAllChromStateOR( FDR_Hyper, 
                                  crom_data[,ChrStatCols], 
                                  outputdir = "CromStates/OR_FDRHyper", 
                                  outputfile = FilesToEnrich[i], 
                                  plots = TRUE )
      chrom_states_fdr_hypo <- getAllChromStateOR( FDR_Hypo,
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_FDRHypo", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
   }
   if ( BN == TRUE) {
      chrom_states_bn <- getAllChromStateOR( crom_data$Bonferroni, 
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_BN", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
      chrom_states_bn_hyper <- getAllChromStateOR( BN_Hyper, 
                                 crom_data[,ChrStatCols], 
                                 outputdir = "CromStates/OR_BNHyper", 
                                 outputfile = FilesToEnrich[i], 
                                 plots = TRUE )
      chrom_states_bn_hypo <- getAllChromStateOR( BN_Hypo, 
                                crom_data[,ChrStatCols], 
                                outputdir = "CromStates/OR_BNHypo", 
                                outputfile = FilesToEnrich[i], 
                                      plots = TRUE )
   }
}

\

Specific molecular enrichments for placenta

\

In the case of working with placenta, several enrichments are performed. All they can be conducted applying a Fisher test or a Hypergeometric test, and also if Betas of the association is provided, then they are done for all CpGs as well as for hypo- and hyper-methylated CpGs.

\

```rEnrichment flowchart. Detailed Placenta enrichment", fig.pos='ht'} include_graphics("imgs/enrich/placenta.png") 

\ 

**A) ROADMAP fetal placenta chromatin states **

Enrichment for fetal placenta chromatin states is based on the data released from ROADMAP:

-  FP_15_E091 (15 chromatin states)
-  FP_18_E091 (18 chromatin states)

\ 

```r

## -- ROADMAP  -  Regulatory feature enrichment analysis - PLACENTA
## -----------------------------------------------------------------

# Convert to Genomic Ranges
data.GRange <- GRanges(
   seqnames = Rle(data$chr),
   ranges=IRanges(data$pos, end=data$pos),
   name=data$CpGs,
   chr=data$chromosome,
   pos=data$pos
)
names(data.GRange) <- data.GRange$name

# Find overlaps between CpGs and Fetal Placenta (States 15 and 18)
over15 <- findOverlapValues(data.GRange, FP_15_E091 )

if (enrichFP18 == TRUE){
   over18 <- findOverlapValues(data.GRange, FP_18_E091 )
   # Add states 15 and 18 to data.GRange file
   # and write to a file : CpGs, state15 and state18
   data.chrstates <- c(mcols(over15$ranges), over15$values, over18$values)
   colnames(data.chrstates)[grep("States",colnames(data.chrstates))] <-  
      c("States15_FP", "States18_FP")
} else {
   # Add states 15 to data.GRange file and write to a file : CpGs, state15
   data.chrstates <- c(mcols(over15$ranges), over15$values)
   colnames(data.chrstates)[grep("States",colnames(data.chrstates))] <-  
      c("States15_FP")
}

# Merge annotated data with chromatine states with states with data
crom_data <- merge(data, data.chrstates, by.x = "CpGs", by.y = "name" )

fname <- paste0("ChrSates_Pla_data/List_CpGs_",
                tools::file_path_sans_ext(basename(FilesToEnrich[i])),
                "_annot_plac_chr_states.txt")
dir.create("ChrSates_Pla_data", showWarnings = FALSE)
write.table( crom_data, fname, quote=F, row.names=F, sep="\t")

## --  Fisher Test - States15_FP - BN,  BN_hyper and BN_hypo 
## (Depletion and Enrichment)
States15FP_bn <- getAllFisherTest(crom_data$Bonferroni,
                               crom_data$States15_FP, 
                               outputdir = "ChrSates_15_Pla/Fisher_BN", 
                               outputfile = FilesToEnrich[i])
States15FP_bnhyper <- getAllFisherTest(BN_Hyper, 
                                 crom_data$States15_FP, 
                                 outputdir = "ChrSates_15_Pla/Fisher_BNHyper", 
                                 outputfile = FilesToEnrich[i])
States15FP_bnhypo <- getAllFisherTest(BN_Hypo, 
                                crom_data$States15_FP, 
                                outputdir = "ChrSates_15_Pla/Fisher_BNHypo", 
                                outputfile = FilesToEnrich[i])


## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = States15FP_bn, 
                                bn_hypo = States15FP_bnhypo, 
                                bn_hyper = States15FP_bnhyper),
                           outputdir = "ChrSates_15_Pla", 
                           outputfile = FilesToEnrich[i])

\

```rChromatine states 15 for placenta - Fisher test for Hyper and Hypo methylated CpGs", fig.pos='ht'} include_graphics("imgs/enrich/chr15pla.png") 

\ 

**B) Placenta partially methylated domains (PMDs) **

Placenta partially methylated domains (PMDs) are large regions that cover approximately 37\% of the human genome, which are partially methylated and contain placenta-specific repressed genes. Enrichment for placenta PMDs is based on the list provided in [@schroeder2013human]

\ 


```r

## -- Partially Methylated Domains (PMDs) PLACENTA
## ------------------------------------------------

# Create genomic ranges from PMD data
PMD.GRange <- getEnrichGenomicRanges(PMD_placenta$Chr_PMD, 
                                     PMD_placenta$Start_PMD, 
                                     PMD_placenta$End_PMD)

# Find overlaps between CpGs and PMD (find subject hits, query hits )
overPMD <- findOverlapValues(data.GRange, PMD.GRange )

#Create a data.frame with CpGs and PMDs information
mdata <- as.data.frame(cbind(DataFrame(CpG = data.GRange$name[overPMD$qhits]), 
                             DataFrame(PMD = PMD.GRange$name[overPMD$shits])))

# Merge with results from meta-analysis (A2)
crom_data <- merge(crom_data, mdata, by.x="CpGs", by.y="CpG",all=T)
# crom_data <- crom_data[order(crom_data$p.value),]

# CpGs with PMD as NA
PMD_NaN <- ifelse(is.na(crom_data$PMD),'IsNA','NotNA' )


## --  Fisher Test - PMD - BN,  BN_hyper and BN_hypo  
## (Full data ) (Depletion and Enrichment)
PMD_bn <- getAllFisherTest(crom_data$Bonferroni, 
                           PMD_NaN, 
                           outputdir = "PMD_Pla/Fisher_BN", 
                           outputfile = FilesToEnrich[i])
PMD_bnhyper <- getAllFisherTest(BN_Hyper, 
                                PMD_NaN, 
                                outputdir = "PMD_Pla/Fisher_BNHyper", 
                                outputfile = FilesToEnrich[i])
PMD_bnhypo <- getAllFisherTest(BN_Hypo, 
                               PMD_NaN, 
                               outputdir = "PMD_Pla/Fisher_BNHypo", 
                               outputfile = FilesToEnrich[i])

 ## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = PMD_bn, 
                                bn_hypo = PMD_bnhypo, 
                                bn_hyper = PMD_bnhyper),
                           outputdir = "PMD_Pla", 
                           outputfile = FilesToEnrich[i])

\

```rPartial Metilated Domains for placenta - Fisher test for Hyper and Hypo methylated CpGs", fig.pos='ht'} include_graphics("imgs/enrich/pmd_pla.png") 

\ 

**C) Placenta imprinted regions **

Placenta germline differentially methylated regions (gDMRs) are regions of the genome which show differential methylation according to the parental origin of the genome. They are associated with expression of imprinted genes, genes which are expressed in a parent-of-originspecific manner. Enrichment for placenta imprinted regions is based on the list provided in  [@hamada2016allele]

\ 

```r
## -- Imprinting Regions PLACENTA
## -------------------------------

# Create genomic ranges from DMR data
DMR.GRange <- getEnrichGenomicRanges(IR_Placenta$Chr_DMR, 
                                     IR_Placenta$Start_DMR, 
                                     IR_Placenta$End_DMR)

# Find overlaps between CpGs and DMR (find subject hits, query hits )
overDMR <- findOverlapValues(data.GRange, DMR.GRange )

#Create a data.frame with CpGs and DMRs information
mdata <- as.data.frame(cbind(DataFrame(CpG = data.GRange$name[overDMR$qhits]), 
                             DataFrame(DMR = DMR.GRange$name[overDMR$shits])))

# Merge with results from meta-analysis (A2)
crom_data <- merge(crom_data, mdata, by.x="CpGs", by.y="CpG",all=T)

# CpGs with DMR as NA
DMR_NaN <- ifelse(is.na(crom_data$DMR.y),'IsNA','NotNA' )

## --  Fisher Test - DMR - BN,  BN_hyper and BN_hypo  
## (Full data ) (Depletion and Enrichment)
DMR_bn <- getAllFisherTest(crom_data$Bonferroni,
                           DMR_NaN, 
                           outputdir = "DMR_Pla/Fisher_BN", 
                           outputfile = FilesToEnrich[i])
DMR_bnhyper <- getAllFisherTest(BN_Hyper, 
                                DMR_NaN, 
                                outputdir = "DMR_Pla/Fisher_BNHyper", 
                                outputfile = FilesToEnrich[i])
DMR_bnhypo <- getAllFisherTest(BN_Hypo, 
                               DMR_NaN, 
                               outputdir = "DMR_Pla/Fisher_BNHypo", 
                               outputfile = FilesToEnrich[i])

 ## --  Plot collapsed data HyperGeometric Test - States15_FP - BN
plot_TestResults_Collapsed(list(bn = DMR_bn, 
                                bn_hypo = DMR_bnhypo, 
                                bn_hyper = DMR_bnhyper),
                           outputdir = "DMR_Pla", outputfile = FilesToEnrich[i])

\

```rImprinted Regions for placenta - Fisher test for Hyper and Hypo methylated CpGs", fig.pos='ht'} include_graphics("imgs/enrich/iregions.png") 

```r
setwd("/Users/mailos/Library/Mobile Documents/com~apple~CloudDocs/PROJECTES/Treballant/EASIER/vignettes")

\

References {#references .unnumbered}



isglobal-brge/EASIER documentation built on Sept. 25, 2024, 10:50 p.m.

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